Search Results for author: Hieu Pham

Found 34 papers, 15 papers with code

DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining

2 code implementations NeurIPS 2023 Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V. Le, Tengyu Ma, Adams Wei Yu

The mixture proportions of pretraining data domains (e. g., Wikipedia, books, web text) greatly affect language model (LM) performance.

Language Modelling

Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata

no code implementations18 Apr 2023 Tue M. Cao, Nhat H. Tran, Phi Le Nguyen, Hieu Pham

This work discusses the use of contrastive learning and deep learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals.

Contrastive Learning Electrocardiography (ECG)

MixupE: Understanding and Improving Mixup from Directional Derivative Perspective

1 code implementation27 Dec 2022 Yingtian Zou, Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.

Data Augmentation

Combined Scaling for Zero-shot Transfer Learning

no code implementations19 Nov 2021 Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, Mingxing Tan, Quoc V. Le

Second, while increasing the dataset size and the model size has been the defacto method to improve the performance of deep learning models like BASIC, the effect of a large contrastive batch size on such contrastive-trained image-text models is not well-understood.

Classification Contrastive Learning +3

WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting

no code implementations17 Mar 2021 Saeed Khaki, Nima Safaei, Hieu Pham, Lizhi Wang

To help mitigate this data collection bottleneck in wheat breeding, we propose a novel deep learning framework to accurately and efficiently count wheat heads to aid in the gathering of real-time data for decision making.

Decision Making Head Detection

Meta Back-translation

1 code implementation ICLR 2021 Hieu Pham, Xinyi Wang, Yiming Yang, Graham Neubig

Back-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data.

Machine Translation Meta-Learning +2

Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

4 code implementations11 Feb 2021 Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, YunHsuan Sung, Zhen Li, Tom Duerig

In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset.

 Ranked #1 on Image Classification on VTAB-1k (using extra training data)

Cross-Modal Retrieval Fine-Grained Image Classification +6

AutoDropout: Learning Dropout Patterns to Regularize Deep Networks

1 code implementation5 Jan 2021 Hieu Pham, Quoc V. Le

As a result, these conventional methods are less effective than methods that leverage the structures, such as SpatialDropout and DropBlock, which randomly drop the values at certain contiguous areas in the hidden states and setting them to zero.

Image Classification Language Modelling +1

Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning

no code implementations5 Dec 2020 Saeed Khaki, Hieu Pham, Lizhi Wang

A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields.

Transfer Learning

Towards Domain-Agnostic Contrastive Learning

no code implementations9 Nov 2020 Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le

Despite recent success, most contrastive self-supervised learning methods are domain-specific, relying heavily on data augmentation techniques that require knowledge about a particular domain, such as image cropping and rotation.

Contrastive Learning Data Augmentation +3

Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour

no code implementations30 Oct 2020 Arissa Wongpanich, Hieu Pham, James Demmel, Mingxing Tan, Quoc Le, Yang You, Sameer Kumar

EfficientNets are a family of state-of-the-art image classification models based on efficiently scaled convolutional neural networks.

Image Classification Playing the Game of 2048

High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks

no code implementations23 Oct 2020 Saeed Khaki, Hieu Pham, Ye Han, Wade Kent, Lizhi Wang

The future landscape of modern farming and plant breeding is rapidly changing due to the complex needs of our society.

Image-Based Sorghum Head Counting When You Only Look Once

no code implementations24 Sep 2020 Lawrence Mosley, Hieu Pham, Yogesh Bansal, Eric Hare

Modern trends in digital agriculture have seen a shift towards artificial intelligence for crop quality assessment and yield estimation.

object-detection Object Detection

DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation

no code implementations20 Jul 2020 Saeed Khaki, Hieu Pham, Ye Han, Andy Kuhl, Wade Kent, Lizhi Wang

In this paper, we propose a novel deep learning method for counting on-ear corn kernels in-field to aid in the gathering of real-time data and, ultimately, to improve decision making to maximize yield.

Decision Making

Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting

no code implementations26 Mar 2020 Saeed Khaki, Hieu Pham, Ye Han, Andy Kuhl, Wade Kent, Lizhi Wang

The sliding window approach uses a convolutional neural network (CNN) for kernel detection.

Marketing

Meta Pseudo Labels

9 code implementations CVPR 2021 Hieu Pham, Zihang Dai, Qizhe Xie, Minh-Thang Luong, Quoc V. Le

We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90. 2% on ImageNet, which is 1. 6% better than the existing state-of-the-art.

Meta-Learning Semi-Supervised Image Classification

Optimizing Data Usage via Differentiable Rewards

1 code implementation ICML 2020 Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell, Graham Neubig

To acquire a new skill, humans learn better and faster if a tutor, based on their current knowledge level, informs them of how much attention they should pay to particular content or practice problems.

Image Classification Machine Translation

Semi-supervised Learning by Coaching

no code implementations25 Sep 2019 Hieu Pham, Quoc V. Le

Recent semi-supervised learning (SSL) methods often have a teacher to train a student in order to propagate labels from labeled data to unlabeled data.

Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems

1 code implementation14 Aug 2019 Mohsen Shahhosseini, Guiping Hu, Hieu Pham

To this end, an optimization-based nested algorithm that considers tuning hyperparameters as well as finding the optimal weights to combine ensembles (Generalized Weighted Ensemble with Internally Tuned Hyperparameters (GEM-ITH)) is designed.

BIG-bench Machine Learning regression

Multilingual Neural Machine Translation With Soft Decoupled Encoding

1 code implementation ICLR 2019 Xinyi Wang, Hieu Pham, Philip Arthur, Graham Neubig

Multilingual training of neural machine translation (NMT) systems has led to impressive accuracy improvements on low-resource languages.

Machine Translation NMT +1

A Tree-based Decoder for Neural Machine Translation

1 code implementation EMNLP 2018 Xinyi Wang, Hieu Pham, Pengcheng Yin, Graham Neubig

Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations.

Machine Translation NMT +2

A Hierarchical Model for Device Placement

no code implementations ICLR 2018 Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean

We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices.

Machine Translation Reinforcement Learning (RL) +1

Faster Discovery of Neural Architectures by Searching for Paths in a Large Model

no code implementations ICLR 2018 Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean

We propose Efficient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods.

Neural Architecture Search

Device Placement Optimization with Reinforcement Learning

1 code implementation ICML 2017 Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices.

Language Modelling Machine Translation +3

Neural Combinatorial Optimization with Reinforcement Learning

10 code implementations29 Nov 2016 Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, Samy Bengio

Despite the computational expense, without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.

Combinatorial Optimization reinforcement-learning +2

Effective Approaches to Attention-based Neural Machine Translation

47 code implementations EMNLP 2015 Minh-Thang Luong, Hieu Pham, Christopher D. Manning

Our ensemble model using different attention architectures has established a new state-of-the-art result in the WMT'15 English to German translation task with 25. 9 BLEU points, an improvement of 1. 0 BLEU points over the existing best system backed by NMT and an n-gram reranker.

 Ranked #1 on Machine Translation on 20NEWS (Accuracy metric)

Image-guided Story Ending Generation Machine Translation +3

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